When Missingness Follows the Graph: Beyond Feature Propagation for Missing Features.

arXiv:2610.18113v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) commonly assume that node features are fully observed, despite missing features being pervasive in real-world applications. By leveraging the dependence between node features and gr